Gliomas are the most common primary brain tumors and occur at an incidence of almost 12 per 100,000 people (Landis et al., 1999). Diffuse astrocytoma may be classified (as per WHO classification) as lower-grade diffuse (DA; Grade II), anaplastic (AA; Grade III) and glioblastoma (Grade IV; GBM), in the order of increasing malignancy (Mischel et al., 2001). Currently, these classifications are based on the observed histopathological characteristics of the tumor, which are sometimes subjective and inconsistent. GBM constitutes more than 80% of malignant gliomas (DeAngelis et al., 2001) and patients with GBM have a median survival of less than one year. Current treatments, including surgery, radiation therapy, and chemotherapy, unfortunately have not changed the natural history of these incurable neoplasms; and the prognosis of patients with GBMs has not improved significantly in the past 30 years (Davis et al., 1998). To find new diagnostic and therapeutic strategies, a better understanding of the biological pathway(s) leading to glial tumorigenesis is warranted.
Astrocytoma development is known to involve accumulation of a series of genetic alterations (Nagane et al., 1997) similar to other cancers. Identification of many of the genes involved in astrocytoma development, using standard molecular approaches, has helped to understand the process of astrocytoma genesis and progression (Louis and Gusella, 1995). Frequent amplification of epidermal growth factor receptor (EGFR) (Hill et al., 1999; Brock and Bower, 1997), platelet derived growth factor receptor (PDGFR) (Hermanson et al., 1992; Hermanson et al., 1996; Maxwell et al., 1990; Westermark et al., 1995; Fleming et al., 1992), amplification of chromosome 12q region, which carries the cdk4 gene (Nagane et al., 1997; Hill et al., 1999) and alterations in chromosomes 1p, 9p, 10, 17p, 19q, and 22q have frequently been found in these tumors. In addition, mutations in the tumor suppressor gene p53 were found to be associated with chromosome 17p alterations in low grade and progressive astrocytoma (Maher et al., 2001; Phatak et al., 2002). Inactivation of the cdk inhibitor p16 INK4a residing in chromosome 9p, is very common in sporadic astrocytoma, occurring in 50-70% of high-grade gliomas and 90% of GBM cell lines (James et al., 1991; Olopade et al., 1992). LOH in chromosome 10 is one of the most frequent alterations in GBM and is accompanied by the loss of PTEN/MMAC gene (Hill et al., 1999; Li et al., 1997).
Despite all this information about astrocytoma, our understanding of astrocytoma development is not sufficient enough to improve prognosis for GBM patients. A more global, systematic understanding of expression patterns of various genes and their downstream gene products in astrocytoma will hopefully provide new diagnostic and therapeutic targets. Towards this, a number of studies have reported the gene expression profile of astrocytoma (Liau et al., 2000; Sallinen et al., 2000; Rickman et al., 2001; Ljubimova et al., 2001; Watson et al., 2001; Tanwar et al., 2002; Fathallah-Shaykh et al., 2002; Nutt et al., 2003; Wang et al., 2003; Godard et al., 2003).
It is also desirable to be able to target specific therapeutic modalities to pathogenetically distinct tumor types to maximize efficacy and minimize toxicity to the patient. (Golub et al., 1999; Kudoh et al., 2000). Previously, cancer classification has been based primarily on the morphological appearance of tumor cells. But this has serious limitations, because tumors with similar histopathological appearance can follow significantly different clinical courses and show different responses to therapy. For example, based on histopathological appearance, astrocytoma grade IV cannot consistently be distinguished from astrocytoma grade III. Immunophenotyping for brain tumors has defined and refined diagnosis, e.g., distinguishing oligoastrocytoma from astrocytomas, and high-grade from low-grade astrocytomas. However, differential protein expression (GFAP, vimentin, synaptophysin, nestin) has not helped to improve therapeutic approaches. Prediction of transitions from low- to high-grade astrocytomas is difficult to make with currently available markers (De Girolami et al., 1994).
(Tews and Nissen, 1998-99) reported that immunohistochemical detection of various cancer-associated markers failed to reveal significant differential expression patterns among primary and secondary glioblastomas and precursor tumors; there was also no intra-individual constant expression pattern during glioma progression or correlation with malignancy.
GBMs have been further subdivided into the primary or secondary GBM subtypes on the basis of clinical and molecular profile. Primary GBMs (pGBM) account for the most of GBM cases, occurring in older patients, while secondary GBMs (sGBM) are quite rare and tend to occur in patients below the age of 45 year. Primary GBM presents in an acute de novo manner with no evidence of a prior symptoms or antecedent lower grade pathology. In contrast, secondary GBM results from the progressive malignant transformation of a DA or AA (Ohgaki and Kleihues, 2007). Remarkably, despite their distinct clinical profiles, primary and secondary GBMs are morphologically and clinically indistinguishable as reflected by an equally poor prognosis when adjusted for patient age (Ohgaki and Kleihues, 2007). However, although these GBM subtypes achieve a common clinical endpoint, recent studies have identified markedly different transcriptional patterns and DNA copy number variations between them (Furnari et al., 2007, Somasundaram et al., 2005). These molecular distinctions make obvious the need to change the current standardized clinical management of these truly distinct entities toward one of rational application of targeted therapies directed towards appropriate molecular subclasses. Furthermore, sGBMs most often have areas of grade III tumor within them and it is necrosis and/or microvascular proliferation that histologically confers the diagnosis of GBM in these tumors. In the event that these areas are missed due to sampling problems, one would still consider the tumor as an AA tumor. It is in such instances that molecular sub-classification proves to be of immense use.
Through miRNA gene expression profiling and real-time quantitative PCR, we have found several differentially regulated miRNAs as diagnostic markers to differentiate, (1) Malignant astrocytoma and Normal brain, (2) Glioblastoma and Anaplastic Astrocytoma, (3) Secondary Glioblastoma and Primary Glioblastoma, (4) Progressive pathway and de novo pathway, and in addition to identifying grade specific miRNAs, inventors identified a 24 miRNA expression signature set that precisely differentiated GBM from AA with an accuracy rate of 95%. By this they would be able to administer appropriate treatment by classifying GBM and AA. These and other benefits are provided by the present invention.
Currently, the classification of grades of Glioma, types of GBMs is based on the observed histopathological characteristics of the tumor.
The classification of grades based on the observed histopathological characteristics of the tumor, are sometimes subjective and inconsistent. But this has serious limitations, because tumors with similar histopathological appearance can follow significantly different clinical courses and show different responses to therapy. For example, based on histopathological appearance, astrocytoma grade IV cannot consistently be distinguished from astrocytoma grade III. Due to the inconsistent classification of grades there is probability of administering inappropriate treatment, which may further decrease the patient's chances of survival. GBM constitutes more than 80% of malignant gliomas (DeAngelis et al., 2001) and patients with GBM have a median survival of less than one year. Current treatments, including surgery, radiation therapy, and chemotherapy, unfortunately have not changed the natural history of these incurable neoplasms; and the prognosis of patients with GBMs has not improved significantly in the past 30 years.
In order to obtain a histology independent miRNA expression signature to differentiate. GBM from AA tumors, we have analyzed the expression of 756 miRNAs by microarray in 13 AA and 29 GBM tumor samples. In addition to identifying grade specific miRNAs, we were able to identify a 24 miRNA expression signature set that precisely differentiated GBM from AA with an accuracy rate of 95%.
Through microarray and real-time quantitative PCR, we found several differentially regulated miRNAs to be specific markers for distinguishing and diagnosing (1) Malignant Astrocytoma and Normal Brain sample, (2) Glioblastoma and Anaplastic astrocytoma, (3) Secondary Glioblastoma and Primary Glioblastoma, (4) Progressive pathway and de novo pathway, thus, to be able to administer appropriate treatment. The method can also be applied to monitor the effectiveness of anti-cancer treatments.